DL-Art-School/codes/scripts/audio/spleeter_split_voice_and_background.py

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from scipy.io import wavfile
from spleeter.separator import Separator
from tqdm import tqdm
from data.util import find_audio_files
import os
import os.path as osp
from spleeter.audio.adapter import AudioAdapter
import numpy as np
# Uses spleeter to divide audio clips into one of two bins:
# 1. Audio has little to no background noise, saved to "output_dir"
# 2. Audio has a lot of background noise, bg noise split off and saved to "output_dir_bg"
if __name__ == '__main__':
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src_dir = 'F:\\split\\joe_rogan'
output_dir = 'F:\\split\\cleaned\\joe_rogan'
output_dir_bg = 'F:\\split\\background-noise\\joe_rogan'
output_sample_rate=22050
os.makedirs(output_dir_bg, exist_ok=True)
os.makedirs(output_dir, exist_ok=True)
audio_loader = AudioAdapter.default()
separator = Separator('spleeter:2stems')
files = find_audio_files(src_dir, include_nonwav=True)
for e, file in enumerate(tqdm(files)):
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#if e < 406500:
# continue
file_basis = osp.relpath(file, src_dir)\
.replace('/', '_')\
.replace('\\', '_')\
.replace('.', '_')\
.replace(' ', '_')\
.replace('!', '_')\
.replace(',', '_')
if len(file_basis) > 100:
file_basis = file_basis[:100]
try:
wave, sample_rate = audio_loader.load(file, sample_rate=output_sample_rate)
except:
print(f"Error with {file}")
continue
sep = separator.separate(wave)
vocals = sep['vocals']
bg = sep['accompaniment']
vmax = np.abs(vocals).mean()
bmax = np.abs(bg).mean()
# Only output to the "good" sample dir if the ratio of background noise to vocal noise is high enough.
ratio = vmax / (bmax+.0000001)
if ratio >= 25: # These values were derived empirically
od = output_dir
os = wave
elif ratio <= 1:
od = output_dir_bg
os = bg
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else:
continue
# Strip out channels.
if len(os.shape) > 1:
os = os[:, 0] # Just use the first channel.
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wavfile.write(osp.join(od, file_basis, f'{e}.wav'), output_sample_rate, os)